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Title: Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation
Authors: Gupta S.
Deep K.
Published in: Neural Computing and Applications
Abstract: Artificial bee colony (ABC) algorithm is an efficient biological-inspired optimization method, which mimics the foraging behavior of honey bees to solve the complex and nonlinear optimization problems. However, in some cases, it suffers from inefficient exploration, low exploitation and slow convergence rate. These shortcomings cause the problem of stagnation at local optimum which is dangerous in determining the true solution (optima) of the problem. Therefore, in the present paper, an attempt has been made toward the removal of the drawbacks from the classical ABC by proposing a novel hybrid method called SCABC algorithm. The SCABC algorithm hybridizes the ABC with sine cosine algorithm (SCA) to upgrade the level of exploitation and exploration in the classical ABC algorithm. The SCA is a recently introduced algorithm, which uses the trigonometric functions sine and cosine to perform the search. The validation of the SCABC algorithm is performed on a well-known benchmark set of 23 optimization problems. The various analysis metrics such as statistical, convergence and performance index analysis verify the better search ability of the SCABC as compared to classical ABC, SCA. The comparison with some other optimization algorithms demonstrates a comparatively better state of exploitation and exploration in the SCABC algorithm. Moreover, the SCABC is also employed on multilevel thresholding problems. The various performance measures demonstrate the efficacy of the SCABC algorithm in determining the optimal thresholds of gray images. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
Citation: Neural Computing and Applications (2019), (): -
Issue Date: 2019
Publisher: Springer London
Keywords: Artificial bee colony (ABC) algorithm
Hybrid algorithms
Multilevel thresholding
Sine cosine algorithm (SCA)
ISSN: 9410643
Author Scopus IDs: 57209786185
Author Affiliations: Gupta, S., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
Funding Details: The first author would like to thank Ministry of Human Resources, Government of India, for funding this research. Grant No. MHR-02-41-113-429.
Corresponding Author: Gupta, S.; Department of Mathematics, Indian Institute of Technology RoorkeeIndia; email:
Appears in Collections:Journal Publications [MA]

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